You’ll work on a fast-moving AI team, owning problems from initial exploration through production.

Turn messy financial data and ambiguous problems into working AI products, starting with simple baselines and adding complexity only when it earns its keep

Build evaluations that reflect real user outcomes, then use error analysis, ablations, and production feedback to make the system better

Make practical tradeoffs across model quality, cost, latency, determinism, reliability, and maintainability

Partner closely with product and engineering to build AI features that take real work off finance teams’ plates

Strong statistical and machine learning fundamentals, with good judgment about when the answer is classical ML, an LLM, agents, or something in between

Experience shipping ML or AI systems end-to-end, from data and evaluation through production

Comfort making progress with noisy data, weak labels, incomplete specifications, and imperfect supervision

Experience across several of: classical ML, embeddings, retrieval and reranking, similarity search, model evaluation, LLM applications, and agentic systems

Strong Python skills and the ability to contribute to production software; TypeScript or modern web application experience is a plus

Experience and education

We welcome a range of backgrounds. Successful candidates will typically have one of the following:

Research Engineer, AI/ML

Responsibilities

Qualification

Required

Benefits

Company

Tabs

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